Abstract
The diversity of SLAM benchmarks affords extensive testing of SLAM algorithms to understand their performance, individually or in relative terms. The ad-hoc creation of these benchmarks does not necessarily illuminate the particular weak points of a SLAM algorithm when performance is evaluated. In this paper, we propose to use a decision tree to identify challenging benchmark properties for state-of-the-art SLAM algorithms and important components within the SLAM pipeline regarding their ability to handle these challenges. Establishing what factors of a particular sequence lead to track failure or degradation relative to these characteristics is important if we are to arrive at a strong understanding for the core computational needs of a robust SLAM algorithm. Likewise, we argue that it is important to profile the computational performance of the individual SLAM components for use when benchmarking. In particular, we advocate the use of time-dilation during ROS bag playback, or what we refer to as slo-mo playback. Using slo-mo to benchmark SLAM instantiations can provide clues to how SLAM implementations should be improved at the computational component level. Three prevalent VO/SLAM algorithms and two low-latency algorithms of our own are tested on selected typical sequences, which are generated from benchmark characterization, to further demonstrate the benefits achieved from computationally efficient components.
Abstract (translated)
SLAM基准的多样性提供了对SLAM算法的广泛测试，以单独或相对地了解它们的性能。在评估性能时，这些基准的特别创建并不一定说明SLAM算法的特殊弱点。在本文中，我们建议使用决策树来识别最先进的SLAM算法的具有挑战性的基准属性，以及SLAM管道中有关它们处理这些挑战的能力的重要组件。如果我们要对鲁棒SLAM算法的核心计算需求有一个很强的理解，那么确定特定序列的哪些因素会导致与这些特性相关的跟踪故障或退化是很重要的。同样，我们认为，在进行基准测试时，分析单个SLAM组件的计算性能非常重要。特别是，我们提倡在ROS包回放期间使用时间膨胀，或者我们称之为SLO-MO回放。使用slo-mo对slam实例进行基准测试，可以提供在计算组件级别如何改进slam实现的线索。在基准特性生成的典型序列上测试了三种流行的VO/SLAM算法和两种我们自己的低延迟算法，进一步证明了计算效率组件所带来的好处。
URL
https://arxiv.org/abs/1905.07808
